Scaling Up Charities using Ai and Machine Learning

The concept of an artificial mind that can think by itself has always been part of human imagination, and the field of Artificial Intelligence has been a mainstay of academia since the 1950s, when computer scientist Alan Turing first asked the question, “can machines do what we, as thinking entities, do?

We always ask the question; “what is the problem that needs to be solve” before we look at the technology, and one of the areas we agree will most benefit from the promise of Ai and optimisation is the third sector — Charities, Social Enterprises, and NGOs.

The potential long-term financial support in reducing costs while also running 24 hours a day, 365 days a year services, with a reduced workforce, is a challenge most grass-roots enterprises face. Grossly underfunded, and with high-demand on support based services, Ai might be the single best opportunity to help tens of thousands of vulnerable people. Technology analyst Gartner projects that more than 85% of customer interactions around the world will be managed without a human by 2020 — charities need to be able to also tap into that opportunity to scale up and stay relevant.

In a UK Charity Digital Skills 2018 report, 77% of respondents said that they would like to see leadership teams develop “a clear understanding of what technology could achieve”.

Perhaps the most useful example of Ai in the charity context is the work we do in chat and voice-based bots. These little NLP-powered, text-based conversational interfaces are being harnessed by many charities to provide services 24 hours a day when a phone or chat services are busy or closed. These bespoke ‘virtual assistants’ can provide vital information to vulnerable people living with various conditions, or people who often feel too ashamed to discuss personal issues with another human.

Predictive algorithms will also help improve human decision-making by mining large amounts of insight from similar cases and issues to provide recommendations based on predicted outcomes — Tailoring advice based on individuals stated needs becomes a big opportunity in this space. For information on things like acute mental health issues, where there is a premium on people being able to access the help and guidance at crisis moments (which often occurs in the middle of the night), so bots offer a real advantage over human operator-delivered advice services in these use-cases.

Real-time monitoring that harnesses data and presents it immediately, in real-time would allow decisions to be made with the most up-to-date information, rather than based on months or years old data. But of course, with any new technology the risks can be high, and given that most charities operate with minimal resources, it is going to be difficult for them to justify taking speculative gambles and become early adopters of technology like Ai. Which is why partnering with companies like Us, to own that strategy can often be the safest approach in mitigating and reducing risk.

The Future of Charity

Leadership ─ both at senior management and a trustee level ─ is also absolutely vital when it comes to utilising Ai. Leadership teams need to understand key trends and drivers and help articulate a clear vision of how the organisation will adapt to the new challenges and opportunities.

There will, of course, be huge cultural issues to contend with too. People in leadership roles are likely to be older and less familiar with the technology that many “digitally native” counterparts may have. This is particularly true in groups of trustees: research published by the Charity Commission for England & Wales found that the average age of trustees was 55-64 (and this increased to 65-74 in the smallest charities), so creating tangible demonstrations of the technology will also be crucial to winning hearts and minds.

As well as service on the “demand” side of the charity equation (i.e. data that enables us to understand needs better), there is also the questions about the “supply” side (i.e. data that enables us to assess which organisations and interventions are most active at addressing given needs).

The other major barrier for charities is in finding the kind of data required to make machine learning viable. To make machines that process like a human, you need large amounts of clean, usable data on which to train the algorithms. It might be that in the future we see charities pooling anonymised data together in a large data warehouse that collect vast amounts of data from different sources across the sector or issue area and quickly analyse it together. If charities have a great data strategy, and a vision of where they need to be, the opportunity for ML to determine patterns in data that can be used to design new interventions is enormous.

Overall, we’re excited about automated support and other Ai applications within the charity space. If you would like to find out more about how we can help you to map out your journey in this space, get in touch. We have a series of easy to engage with strategic steps and products aimed directly at the third sector.

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